The aim of the project is predicting how biomolecules bind, like proteins and DNA, is crucial for breakthroughs in genetics, drug discovery, and disease research. Traditional methods are slow and costly. Our project uses AI to predict binding strength directly from 3D structures, drastically cutting time and costs. By training a deep learning model on a specific protein and many mutated DNA variants, we can quickly determine the strength of the interaction. This helps researchers scale, test ideas faster, improve models sooner and expedite scientific progress.

Alberto De Lazzari presenta esempi concreti di come l'analisi delle connessioni, con GraphAI e Graph Neural Networks, ha fatto la differenza in LARUS Business Automation.

This project leverages graph neural networks (GNNs) to predict the antiviral potency of small molecules by analyzing their molecular structures and interactions. By learning complex patterns in chemical data, GNNs can identify promising drug candidates with high efficacy against viruses. The insights gained from this approach aim to accelerate antiviral drug discovery and development.